




"""simple docstring"""




import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


lowerCAmelCase				:     Tuple					=				"""\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

lowerCAmelCase				:     Tuple					=				"""\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

lowerCAmelCase				:     List[str]					=				"""
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION							)
class        __magic_name__				(			datasets.Metric							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return datasets.MetricInfo(
							    description=_DESCRIPTION						,					citation=_CITATION						,					homepage="""https://github.com/krishnap25/mauve"""						,					inputs_description=_KWARGS_DESCRIPTION						,					features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""						,					id="""sequence"""					),
							            """references""": datasets.Value("""string"""						,					id="""sequence"""					),
							        }					)						,					codebase_urls=["""https://github.com/krishnap25/mauve"""]						,					reference_urls=[
							        """https://arxiv.org/abs/2102.01454""",
							        """https://github.com/krishnap25/mauve""",
							    ]						,					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a=None						,					_a=None						,					_a=None						,					_a=None						,					_a="auto"						,					_a=-1						,					_a=0.9						,					_a=5						,					_a=500						,					_a="gpt2-large"						,					_a=-1						,					_a=1_024						,					_a=25						,					_a=5						,					_a=True						,					_a=25						,					):





							"""simple docstring"""
							lowerCamelCase      =   compute_mauve(
							    p_text=_a						,					q_text=_a						,					p_features=_a						,					q_features=_a						,					p_tokens=_a						,					q_tokens=_a						,					num_buckets=_a						,					pca_max_data=_a						,					kmeans_explained_var=_a						,					kmeans_num_redo=_a						,					kmeans_max_iter=_a						,					featurize_model_name=_a						,					device_id=_a						,					max_text_length=_a						,					divergence_curve_discretization_size=_a						,					mauve_scaling_factor=_a						,					verbose=_a						,					seed=_a						,					)
							return out









"""simple docstring"""




def        a__					(							snake_case__ = 50_00_00_00							)		->   int:
			lowerCamelCase      =   set()
			lowerCamelCase      =   int((limit - 24) ** (1 / 2)							)

			lowerCamelCase      =   set(range(3			,					prime_square_limit + 1			,					2							)							)
			primes.add(2							)
			for p in range(3			,					prime_square_limit + 1			,					2							):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p			,					prime_square_limit + 1			,					snake_case__							)							)							)

			for primea in primes:
						lowerCamelCase      =   primea * primea
						for primea in primes:
									lowerCamelCase      =   primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												lowerCamelCase      =   primea * primea * primea * primea
												lowerCamelCase      =   square + cube + tetr
												if total >= limit:
															break
												ret.add(snake_case__							)

			return len(snake_case__							)


if __name__ == "__main__":
					print(F"""{solution() = }""")



